ABSTRACT The already funded K23 is focused on investigating the brain-immune interactions associated with maladaptive eating behaviors (disinhibited ingestive behaviors [DIB]). DIB is an important factor in the underlying pathophysiology observed in 25-37% of obese individuals where eating for pleasure overrides eating for homeostatic needs. This R03 application builds on the investigative efforts of the K23 by aiming to identify the role of altered brain gut microbiome (BGM) signaling contributing to DIB in the same obese sample recruited for the K23. I propose to address this goal both in a cross-sectional study, as well as in a longitudinal study determining changes in the BGM axis in response to two interventions: cognitive behavioral therapy (CBT) and a diet intervention (high fiber hypocaloric diet). While the cross-sectional study will identify alterations in BGM interactions between obese females with DIB compared to those without DIB, the longitudinal intervention studies aim to identify biomarkers that predict treatment responses and to identify biologically based subgroups of patients. The CBT intervention is aimed to strengthen prefrontal inhibitory influences on reward networks, thereby reversing maladaptive ingestive behaviors. The CBT intervention will be compared to a control diet intervention which will target the gut microbiome. The primary outcome is the normalization of DIB with the secondary outcome being weight loss. Recruitment will be restricted to females due to the known sex differences in DIB, with higher cravings, poorer weight loss and maintenance outcomes in obese females compared to obese males. The presence of DIB will be based on the Yale Food Addiction Scale (YFAS), a validated measure of disinhibited ingestion. Stool and serum to determine microbial-related measures (16sRNA sequencing, shotgun metagenomics, and metabolomics), and MRI to assess brain alterations in the extended reward network will be collected pre-and post-intervention and will be used to examine disinhibited ingestion-related differences and as predictors of clinical response. Advanced multivariate analytic techniques will be used to integrate data from multiple neuroimaging sources, microbiome and metabolite profiles, and behavioral data. This analysis will determine the unique variance associated with DIB in moderating the altered BGM axis at baseline and after the interventions. Integrating information from multiple central, peripheral, and behavioral sources will help increase the validity of the proposed BGM model and will identify phenotypes at increased risk for DIB and obesity. The proposed studies will focus on: 1) Identifying mechanisms underlying maladaptive eating behaviors in DIB, 2) identifying targets that can be modified by brain and gut targeted interventions, and 3) to generate pilot data for a larger mechanistic R01 proposal. Identifying biological characteristics of a subset of obese patients is essential for the development of more effective, personalized non-surgical treatments, which may be used in conjunction with pharmacological treatments.